Sounding Like a Winner? Prosodic Differences in Post-Match Interviews

2025/06/02 by Sofoklis Kakouros, Haoyu Chen, Kakouros, Sofoklis +1
Arts and Humanities · #Audio and Speech Processing (eess.AS) #Computation and Language (cs.CL) #FOS: Computer and information sciences #FOS: Electrical engineering #Language, Discourse, Communication Strategies #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2506.02283

openalex publication_date 2025/06/02 · openalex created_date 2025/10/14 · openalex updated_date 2026/07/28

Abstract

This study examines the prosodic characteristics associated with winning and losing in post-match tennis interviews. Additionally, this research explores the potential to classify match outcomes solely based on post-match interview recordings using prosodic features and self-supervised learning (SSL) representations. By analyzing prosodic elements such as pitch and intensity, alongside SSL models like Wav2Vec 2.0 and HuBERT, the aim is to determine whether an athlete has won or lost their match. Traditional acoustic features and deep speech representations are extracted from the data, and machine learning classifiers are employed to distinguish between winning and losing players. Results indicate that SSL representations effectively differentiate between winning and losing outcomes, capturing subtle speech patterns linked to emotional states. At the same time, prosodic cues -- such as pitch variability -- remain strong indicators of victory.

Citations

• ExHuBERT: Enhancing HuBERT Through Block Extension and Fine-Tuning on 37 Emotion Datasets
• Speech-based emotion recognition with self-supervised models using attentive channel-wise correlations and label smoothing
• Extracting speaker and emotion information from self-supervised speech models via channel-wise correlations
• WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing
• HuBERT: Self-Supervised Speech Representation Learning by Masked\n Prediction of Hidden Units
• iMiGUE: An Identity-free Video Dataset for Micro-Gesture Understanding and Emotion Analysis
• SUPERB: Speech processing Universal PERformance Benchmark
• Emotion Recognition from Speech Using Wav2vec 2.0 Embeddings
• wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations
• A Unified Approach to Interpreting Model Predictions
• Recognising realistic emotions and affect in speech: State of the art and lessons learnt from the first challenge
• Emotional speech recognition: Resources, features, and methods

Related

• Predicting Sex and Stroke Success -- Computer-aided Player Grunt Analysis in Tennis Matches
• Research on dynamic analysis and prediction model of tennis match based on Bayesian probability and analytic Hierarchy process
• Random forest model identifies serve strength as a key predictor of tennis match outcome
• Enhancing Predictive Accuracy in Tennis: Integrating Fuzzy Logic and CV-GRNN for Dynamic Match Outcome and Player Momentum Analysis
• Prosodic Structure Beyond Lexical Content: A Study of Self-Supervised Learning